MaziyarPanahi/calme-2.2-qwen2-7b
Calme-2.2-Qwen2-7B is a 7 billion parameter causal language model developed by MaziyarPanahi, fine-tuned from the Qwen/Qwen2-7B base model. This iteration aims to enhance the base model's performance across various benchmarks, making it a general-purpose model suitable for a wide range of text generation tasks. It utilizes the ChatML prompt template for instruction following and is available in quantized GGUF formats for efficient deployment.
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Overview
MaziyarPanahi/calme-2.2-qwen2-7b is a fine-tuned version of the Qwen/Qwen2-7B model, developed by MaziyarPanahi. The primary goal of this fine-tuning effort is to achieve improved performance across a broad spectrum of benchmarks, making it a more capable general-purpose language model.
Key Capabilities
- Enhanced General Performance: Aims to surpass the base
Qwen2-7Bmodel in overall benchmark scores. - Instruction Following: Utilizes the
ChatMLprompt template for clear and effective instruction-tuned interactions. - Quantized Versions Available: Offers GGUF quantized models for optimized inference and reduced memory footprint, accessible via the MaziyarPanahi/calme-2.2-qwen2-7b-GGUF repository.
Open LLM Leaderboard Performance
This model has been evaluated on the Open LLM Leaderboard, demonstrating its capabilities across several metrics. Key scores include:
- Avg.: 23.23
- IFEval (0-Shot): 35.97
- BBH (3-Shot): 33.11
- MMLU-PRO (5-shot): 32.21
Detailed results are available on the Open LLM Leaderboard.
Good for
- General text generation and conversational AI applications.
- Developers seeking a fine-tuned 7B parameter model with improved benchmark performance.
- Use cases requiring efficient deployment, leveraging the provided GGUF quantized versions.